Date: (Sun) Jun 05, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
# glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbRFESizes[["RFE.X"]] <- c(96, 112, 120, 124, 128, 129, 130, 131, 132, 133, 135, 138, 142, 157, 187, 247) # accuracy(131) = 0.6285
glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", "Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    glbMdlFamilies[["All.X"]]     <- c("glmnet", "glm")
}
glbMdlFamilies[["All.X.Inc"]] <- c("glmnet")
glbMdlFamilies[["RFE.X"]] <- c("glmnet")

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
glbMdlAllowParallel[["All.X#zv.pca#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X#ica#rcv#glmnet"]] <- FALSE

glbMdlAllowParallel[["RFE.X#zv.pca#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X#ica#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X#nzv.pca.spatialSign#rcv#glmnet"]] <- FALSE

glbMdlAllowParallel[["Final.RFE.X#zv.pca#rcv#glmnet"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list( # NULL # : default
    "All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods, 
                                              c("knnImpute", "bagImpute", "medianImpute")),
                                    # NULL))
                                    c("nzv.spatialSign")))    
)
glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
                                                    "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Votes_RFEX_cnk04_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "fit.models_1" #default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL #default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Votes_RFEX_cnk04_fit_models_1_fit.models_1.RData" # "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# glbgetModelSelectFormula <- function() {
#     model_evl_terms <- c(NULL)
#     # min.aic.fit might not be avl
#     lclMdlEvlCriteria <- 
#         glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
#     for (metric in lclMdlEvlCriteria)
#         model_evl_terms <- c(model_evl_terms, 
#                              ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
#     if (glb_is_classification && glb_is_binomial)
#         model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
#     model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
#     return(model_sel_frmla)
# }

# glbgetDisplayModelsDf <- function() {
#     dsp_models_cols <- c("id", 
#                     glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
#                     grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
#     dsp_models_df <- 
#         #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
#         orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
#     nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
#     nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
#         nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
#     
# #     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# #     nParams <- nParams[names(nParams) != "avNNet"]    
#     
#     if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
#         print("Cross Validation issues:")
#         warning("Cross Validation issues:")        
#         print(cvMdlProblems)
#     }
#     
#     pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
#     pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
#     
#     # length(pltMdls) == 21
#     png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
#     grid.newpage()
#     pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
#     pltIx <- 1
#     for (mdlId in pltMdls) {
#         print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
#               vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
#                             layout.pos.col = ((pltIx - 1) %% 2) + 1))  
#         pltIx <- pltIx + 1
#     }
#     dev.off()
# 
#     if (all(row.names(dsp_models_df) != dsp_models_df$id))
#         row.names(dsp_models_df) <- dsp_models_df$id
#     return(dsp_models_df)
# }

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##          label step_major step_minor label_minor  bgn end elapsed
## 1 fit.models_1          1          0           0 7.72  NA      NA

Step 1.0: fit models_1

chunk option: eval=

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")

ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")

rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 317.965  NA      NA
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 317.965 317.978
## 2 fit.models_1_All.X          1          1       setup 317.978      NA
##   elapsed
## 1   0.013
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 317.978 317.988
## 3 fit.models_1_All.X          1          2      glmnet 317.988      NA
##   elapsed
## 2    0.01
## 3      NA
## [1] "skipping fitting model: All.X##rcv#glmnet"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 317.988 317.993
## 4 fit.models_1_All.X          1          3         glm 317.994      NA
##   elapsed
## 3   0.005
## 4      NA
## [1] "skipping fitting model: All.X##rcv#glm"
##                    label step_major step_minor label_minor     bgn     end
## 4     fit.models_1_All.X          1          3         glm 317.994 317.998
## 5 fit.models_1_All.X.Inc          1          4       setup 317.999      NA
##   elapsed
## 4   0.004
## 5      NA
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-5
##                    label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_All.X.Inc          1          4       setup 317.999 318.291
## 6 fit.models_1_All.X.Inc          1          5      glmnet 318.292      NA
##   elapsed
## 5   0.292
## 6      NA
## [1] "skipping fitting model: All.X.Inc#nzv.spatialSign#rcv#glmnet"
##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_1_All.X.Inc          1          5      glmnet 318.292 318.301
## 7     fit.models_1_RFE.X          1          6       setup 318.302      NA
##   elapsed
## 6   0.009
## 7      NA
##                label step_major step_minor label_minor     bgn     end
## 7 fit.models_1_RFE.X          1          6       setup 318.302 318.308
## 8 fit.models_1_RFE.X          1          7      glmnet 318.308      NA
##   elapsed
## 7   0.006
## 8      NA
## [1] "skipping fitting model: RFE.X##rcv#glmnet"
##                  label step_major step_minor label_minor     bgn     end
## 8   fit.models_1_RFE.X          1          7      glmnet 318.308 318.316
## 9 fit.models_1_preProc          1          8     preProc 318.316      NA
##   elapsed
## 8   0.008
## 9      NA
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "skipping fitting model: All.X#zv#rcv#glmnet"
## [1] "skipping fitting model: All.X#nzv#rcv#glmnet"
## [1] "skipping fitting model: All.X#BoxCox#rcv#glmnet"
## [1] "skipping fitting model: All.X#YeoJohnson#rcv#glmnet"
## [1] "skipping fitting model: All.X#expoTrans#rcv#glmnet"
## [1] "skipping fitting model: All.X#center#rcv#glmnet"
## [1] "skipping fitting model: All.X#scale#rcv#glmnet"
## [1] "skipping fitting model: All.X#center.scale#rcv#glmnet"
## [1] "skipping fitting model: All.X#range#rcv#glmnet"
## [1] "skipping fitting model: All.X#zv.pca#rcv#glmnet"
## [1] "skipping fitting model: All.X#ica#rcv#glmnet"
## [1] "skipping fitting model: All.X#spatialSign#rcv#glmnet"
## [1] "skipping fitting model: All.X#conditionalX#rcv#glmnet"
## [1] "skipping fitting model: All.X#nzv.spatialSign#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#zv#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#nzv#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#BoxCox#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#YeoJohnson#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#expoTrans#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#center#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#scale#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#center.scale#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#range#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#zv.pca#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#ica#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#spatialSign#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#conditionalX#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#nzv.spatialSign#rcv#glmnet"
## [1] "skipping fitting model: RFE.X#nzv.pca.spatialSign#rcv#glmnet"
##                                      min.elapsedtime.everything
## Random###myrandom_classfr                                 0.265
## MFO###myMFO_classfr                                       0.399
## Max.cor.Y.rcv.1X1###glmnet                                0.753
## Max.cor.Y##rcv#rpart                                      1.624
## Interact.High.cor.Y##rcv#glmnet                           5.284
## All.X##rcv#glm                                           14.312
## RFE.X##rcv#glmnet                                        21.949
## All.X#conditionalX#rcv#glmnet                            22.580
## All.X##rcv#glmnet                                        22.609
## Low.cor.X##rcv#glmnet                                    22.775
## RFE.X#conditionalX#rcv#glmnet                            23.022
## All.X#zv#rcv#glmnet                                      23.568
## RFE.X#zv#rcv#glmnet                                      24.269
## RFE.X#scale#rcv#glmnet                                   25.649
## RFE.X#center#rcv#glmnet                                  25.755
## All.X#scale#rcv#glmnet                                   26.279
## All.X#center#rcv#glmnet                                  26.891
## RFE.X#range#rcv#glmnet                                   27.116
## RFE.X#BoxCox#rcv#glmnet                                  28.258
## All.X#range#rcv#glmnet                                   28.776
## All.X#BoxCox#rcv#glmnet                                  29.405
## RFE.X#center.scale#rcv#glmnet                            29.657
## All.X#center.scale#rcv#glmnet                            30.287
## RFE.X#nzv#rcv#glmnet                                     34.109
## All.X#nzv#rcv#glmnet                                     36.115
## All.X#spatialSign#rcv#glmnet                             36.885
## RFE.X#ica#rcv#glmnet                                     39.908
## All.X#ica#rcv#glmnet                                     41.240
## RFE.X#spatialSign#rcv#glmnet                             46.699
## RFE.X#nzv.spatialSign#rcv#glmnet                         51.794
## All.X#nzv.spatialSign#rcv#glmnet                         52.416
## All.X#YeoJohnson#rcv#glmnet                              58.542
## All.X.Inc#nzv.spatialSign#rcv#glmnet                     67.172
## All.X#expoTrans#rcv#glmnet                               70.569
## RFE.X#expoTrans#rcv#glmnet                               71.462
## RFE.X#zv.pca#rcv#glmnet                                  75.364
## All.X#zv.pca#rcv#glmnet                                  77.239
## RFE.X#YeoJohnson#rcv#glmnet                              84.187
## RFE.X#nzv.pca.spatialSign#rcv#glmnet                    533.967
##                   label step_major step_minor label_minor     bgn     end
## 9  fit.models_1_preProc          1          8     preProc 318.316 319.658
## 10     fit.models_1_end          1          9    teardown 319.658      NA
##    elapsed
## 9    1.342
## 10      NA
##          label step_major step_minor label_minor     bgn     end elapsed
## 1 fit.models_1          1          0           0   7.720 319.664 311.944
## 2   fit.models          1          1           1 319.665      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 319.77  NA      NA
require(reshape2)
## Loading required package: reshape2
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
# print(plt_models_df)
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x= id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols])
##                                      id max.Accuracy.OOB max.AUCROCR.OOB
## 34              RFE.X#zv.pca#rcv#glmnet        0.5829596       0.5816510
## 22     All.X#nzv.spatialSign#rcv#glmnet        0.5820628       0.5787416
## 20         All.X#spatialSign#rcv#glmnet        0.5811659       0.5793099
## 38     RFE.X#nzv.spatialSign#rcv#glmnet        0.5811659       0.5785672
## 23 All.X.Inc#nzv.spatialSign#rcv#glmnet        0.5811659       0.5734523
## 36         RFE.X#spatialSign#rcv#glmnet        0.5802691       0.5794035
## 10                 All.X#nzv#rcv#glmnet        0.5775785       0.5766539
## 26                 RFE.X#nzv#rcv#glmnet        0.5775785       0.5762019
## 13           All.X#expoTrans#rcv#glmnet        0.5775785       0.5745421
## 29           RFE.X#expoTrans#rcv#glmnet        0.5775785       0.5745421
## 28          RFE.X#YeoJohnson#rcv#glmnet        0.5775785       0.5744824
## 12          All.X#YeoJohnson#rcv#glmnet        0.5775785       0.5744824
## 24                    RFE.X##rcv#glmnet        0.5766816       0.5757966
## 25                  RFE.X#zv#rcv#glmnet        0.5766816       0.5757966
## 27              RFE.X#BoxCox#rcv#glmnet        0.5766816       0.5757966
## 30              RFE.X#center#rcv#glmnet        0.5766816       0.5757966
## 31               RFE.X#scale#rcv#glmnet        0.5766816       0.5757966
## 32        RFE.X#center.scale#rcv#glmnet        0.5766816       0.5757966
## 33               RFE.X#range#rcv#glmnet        0.5766816       0.5757966
## 37        RFE.X#conditionalX#rcv#glmnet        0.5766816       0.5757966
## 6                 Low.cor.X##rcv#glmnet        0.5766816       0.5757966
## 7                     All.X##rcv#glmnet        0.5766816       0.5757966
## 9                   All.X#zv#rcv#glmnet        0.5766816       0.5757966
## 11              All.X#BoxCox#rcv#glmnet        0.5766816       0.5757966
## 14              All.X#center#rcv#glmnet        0.5766816       0.5757966
## 15               All.X#scale#rcv#glmnet        0.5766816       0.5757966
## 16        All.X#center.scale#rcv#glmnet        0.5766816       0.5757966
## 17               All.X#range#rcv#glmnet        0.5766816       0.5757966
## 21        All.X#conditionalX#rcv#glmnet        0.5766816       0.5757966
## 18              All.X#zv.pca#rcv#glmnet        0.5748879       0.5798104
## 39 RFE.X#nzv.pca.spatialSign#rcv#glmnet        0.5713004       0.5742305
## 8                        All.X##rcv#glm        0.5650224       0.5687249
## 35                 RFE.X#ica#rcv#glmnet        0.5363229       0.5370894
## 19                 All.X#ica#rcv#glmnet        0.5354260       0.5360626
## 5       Interact.High.cor.Y##rcv#glmnet        0.5345291       0.5242069
## 2             Random###myrandom_classfr        0.5300448       0.5181895
## 3            Max.cor.Y.rcv.1X1###glmnet        0.5300448       0.5102459
## 4                  Max.cor.Y##rcv#rpart        0.5300448       0.5000646
## 1                   MFO###myMFO_classfr        0.5300448       0.5000000
##    max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 34       0.5792308        0.6459313                   0.50
## 22       0.5572148        0.6478785                   0.50
## 20       0.5570372        0.6492270                   0.50
## 38       0.5597528        0.6462317                   0.50
## 23       0.5556503        0.6440612                   0.50
## 36       0.5570372        0.6490772                   0.50
## 10       0.5550900        0.6475806                   0.50
## 26       0.5531816        0.6466826                   0.50
## 13       0.5559554        0.6463076                   0.50
## 29       0.5559554        0.6462327                   0.50
## 28       0.5559554        0.6466820                   0.50
## 12       0.5559554        0.6465321                   0.50
## 24       0.5588180        0.6475052                   0.50
## 25       0.5588180        0.6475052                   0.50
## 27       0.5588180        0.6475052                   0.50
## 30       0.5588180        0.6475052                   0.50
## 31       0.5588180        0.6475052                   0.50
## 32       0.5588180        0.6475052                   0.50
## 33       0.5588180        0.6475052                   0.50
## 37       0.5588180        0.6475052                   0.50
## 6        0.5588180        0.6471308                   0.50
## 7        0.5588180        0.6471308                   0.50
## 9        0.5588180        0.6471308                   0.50
## 11       0.5588180        0.6471308                   0.50
## 14       0.5588180        0.6471308                   0.50
## 15       0.5588180        0.6471308                   0.50
## 16       0.5588180        0.6471308                   0.50
## 17       0.5588180        0.6471308                   0.50
## 21       0.5588180        0.6471308                   0.50
## 18       0.5717247        0.6421158                   0.50
## 39       0.5545298        0.6480297                   0.50
## 8        0.5487933        0.6254219                   0.50
## 35       0.5121737        0.5603719                   0.50
## 19       0.5133442        0.5607462                   0.50
## 5        0.5218093        0.6250526                   0.50
## 2        0.4836608        0.5299798                   0.55
## 3        0.4999322        0.6240737                   0.45
## 4        0.4999322        0.6227308                   0.50
## 1        0.5000000        0.5299798                   0.50
##    opt.prob.threshold.OOB
## 34                   0.50
## 22                   0.60
## 20                   0.60
## 38                   0.60
## 23                   0.60
## 36                   0.60
## 10                   0.60
## 26                   0.55
## 13                   0.55
## 29                   0.55
## 28                   0.55
## 12                   0.55
## 24                   0.55
## 25                   0.55
## 27                   0.55
## 30                   0.55
## 31                   0.55
## 32                   0.55
## 33                   0.55
## 37                   0.55
## 6                    0.55
## 7                    0.55
## 9                    0.55
## 11                   0.55
## 14                   0.55
## 15                   0.55
## 16                   0.55
## 17                   0.55
## 21                   0.55
## 18                   0.50
## 39                   0.55
## 8                    0.65
## 35                   0.55
## 19                   0.55
## 5                    0.70
## 2                    0.55
## 3                    0.65
## 4                    0.65
## 1                    0.50
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(glbgetModelSelectFormula())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fa8afd836d8>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: RFE.X#zv.pca#rcv#glmnet"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(glbgetModelSelectFormula(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- glbgetDisplayModelsDf()
}

if (is.null(glbMdlSelId)) 
    glbMdlSelId <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glbMdlSelId))   
    
# knitr crashes sometimes with plot.glmnet
if (!(is.null(knitr::opts_current$get(name = 'label'))) &&
    (myparseMdlId(glbMdlSelId)$alg == "glmnet"))
    print(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]]) else
    myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## glmnet 
## 
## Pre-processing: principal component signal extraction (243),
##  centered (243), scaled (243), remove (1) 
## Resampling: Cross-Validated (3 fold, repeated 3 times) 
## Summary of sample sizes: 2969, 2969, 2968, 2968, 2970, 2968, ... 
## Resampling results across tuning parameters:
## 
##   alpha  lambda        Accuracy   Kappa    
##   0.100  8.883582e-05  0.6380735  0.2733807
##   0.100  4.123393e-04  0.6380735  0.2733807
##   0.100  1.913910e-03  0.6381484  0.2734989
##   0.100  8.883582e-03  0.6394212  0.2756833
##   0.100  4.123393e-02  0.6436874  0.2823364
##   0.325  8.883582e-05  0.6382981  0.2738119
##   0.325  4.123393e-04  0.6382981  0.2738119
##   0.325  1.913910e-03  0.6386718  0.2744242
##   0.325  8.883582e-03  0.6426389  0.2816353
##   0.325  4.123393e-02  0.6412897  0.2761086
##   0.550  8.883582e-05  0.6385972  0.2744123
##   0.550  4.123393e-04  0.6385972  0.2744123
##   0.550  1.913910e-03  0.6395700  0.2761095
##   0.550  8.883582e-03  0.6440610  0.2840948
##   0.550  4.123393e-02  0.6323820  0.2562114
##   0.775  8.883582e-05  0.6385223  0.2742817
##   0.775  4.123393e-04  0.6385224  0.2743094
##   0.775  1.913910e-03  0.6391210  0.2750959
##   0.775  8.883582e-03  0.6459313  0.2875399
##   0.775  4.123393e-02  0.6232505  0.2355461
##   1.000  8.883582e-05  0.6387470  0.2747400
##   1.000  4.123393e-04  0.6382230  0.2736425
##   1.000  1.913910e-03  0.6410669  0.2788996
##   1.000  8.883582e-03  0.6458553  0.2868669
##   1.000  4.123393e-02  0.6177872  0.2213701
## 
## Accuracy was used to select the optimal model using  the largest value.
## The final values used for the model were alpha = 0.775 and lambda
##  = 0.008883582.
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))
## [1] "RFE.X#zv.pca#rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))
## [1] "RFE.X#zv.pca#rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##       RFE.X.zv.pca.rcv.glmnet.imp          imp
## PC8                  1.000000e+02 1.000000e+02
## PC5                  7.995721e+01 7.995721e+01
## PC10                 6.660214e+01 6.660214e+01
## PC15                 6.321726e+01 6.321726e+01
## PC9                  5.584978e+01 5.584978e+01
## PC26                 5.050796e+01 5.050796e+01
## PC3                  4.474537e+01 4.474537e+01
## PC14                 4.398423e+01 4.398423e+01
## PC57                 3.105066e+01 3.105066e+01
## PC68                 2.684775e+01 2.684775e+01
## PC30                 2.569378e+01 2.569378e+01
## PC88                 2.477208e+01 2.477208e+01
## PC128                2.424400e+01 2.424400e+01
## PC25                 2.371905e+01 2.371905e+01
## PC19                 2.341047e+01 2.341047e+01
## PC124                2.213194e+01 2.213194e+01
## PC52                 2.128227e+01 2.128227e+01
## PC16                 2.054784e+01 2.054784e+01
## PC142                1.751430e+01 1.751430e+01
## PC114                1.650518e+01 1.650518e+01
## PC40                 1.496730e+01 1.496730e+01
## PC23                 1.488607e+01 1.488607e+01
## PC22                 1.452383e+01 1.452383e+01
## PC100                1.448174e+01 1.448174e+01
## PC138                1.321459e+01 1.321459e+01
## PC126                1.295801e+01 1.295801e+01
## PC45                 1.271547e+01 1.271547e+01
## PC101                1.257034e+01 1.257034e+01
## PC2                  1.110115e+01 1.110115e+01
## PC11                 1.060646e+01 1.060646e+01
## PC77                 8.954286e+00 8.954286e+00
## PC6                  8.539962e+00 8.539962e+00
## PC17                 8.207663e+00 8.207663e+00
## PC70                 7.297321e+00 7.297321e+00
## PC47                 6.727046e+00 6.727046e+00
## PC106                6.709574e+00 6.709574e+00
## PC27                 6.583676e+00 6.583676e+00
## PC108                6.523922e+00 6.523922e+00
## PC20                 6.471282e+00 6.471282e+00
## PC95                 6.273683e+00 6.273683e+00
## PC92                 6.234515e+00 6.234515e+00
## PC111                5.951208e+00 5.951208e+00
## PC87                 5.856015e+00 5.856015e+00
## PC117                5.778034e+00 5.778034e+00
## PC144                5.666493e+00 5.666493e+00
## PC54                 5.627894e+00 5.627894e+00
## PC74                 5.526187e+00 5.526187e+00
## PC140                5.171006e+00 5.171006e+00
## PC85                 5.090498e+00 5.090498e+00
## PC118                4.902623e+00 4.902623e+00
## PC109                4.849634e+00 4.849634e+00
## PC44                 4.657604e+00 4.657604e+00
## PC4                  4.495763e+00 4.495763e+00
## PC35                 4.402217e+00 4.402217e+00
## PC84                 3.861871e+00 3.861871e+00
## PC102                3.345513e+00 3.345513e+00
## PC49                 3.217595e+00 3.217595e+00
## PC113                2.752156e+00 2.752156e+00
## PC103                2.182427e+00 2.182427e+00
## PC130                2.081387e+00 2.081387e+00
## PC51                 1.903814e+00 1.903814e+00
## PC33                 1.735816e+00 1.735816e+00
## PC1                  1.487771e+00 1.487771e+00
## PC75                 1.133188e+00 1.133188e+00
## PC123                4.889254e-01 4.889254e-01
## PC99                 3.645912e-01 3.645912e-01
## PC46                 2.206416e-01 2.206416e-01
## PC12                 1.279928e-01 1.279928e-01
## PC133                8.569951e-02 8.569951e-02
## PC137                7.388924e-02 7.388924e-02
## PC37                 2.029163e-02 2.029163e-02
## PC120                7.951659e-03 7.951659e-03
## PC7                  0.000000e+00 0.000000e+00
## PC13                 0.000000e+00 0.000000e+00
## PC18                 0.000000e+00 0.000000e+00
## PC21                 0.000000e+00 0.000000e+00
## PC24                 0.000000e+00 0.000000e+00
## PC28                 0.000000e+00 0.000000e+00
## PC29                 0.000000e+00 0.000000e+00
## PC31                 0.000000e+00 0.000000e+00
## PC32                 0.000000e+00 0.000000e+00
## PC34                 0.000000e+00 0.000000e+00
## PC36                 0.000000e+00 0.000000e+00
## PC38                 0.000000e+00 0.000000e+00
## PC39                 0.000000e+00 0.000000e+00
## PC41                 0.000000e+00 0.000000e+00
## PC42                 0.000000e+00 0.000000e+00
## PC43                 0.000000e+00 0.000000e+00
## PC48                 0.000000e+00 0.000000e+00
## PC50                 0.000000e+00 0.000000e+00
## PC53                 0.000000e+00 0.000000e+00
## PC55                 0.000000e+00 0.000000e+00
## PC56                 0.000000e+00 0.000000e+00
## PC58                 0.000000e+00 0.000000e+00
## PC59                 0.000000e+00 0.000000e+00
## PC60                 0.000000e+00 0.000000e+00
## PC61                 0.000000e+00 0.000000e+00
## PC62                 0.000000e+00 0.000000e+00
## PC63                 0.000000e+00 0.000000e+00
## PC64                 0.000000e+00 0.000000e+00
## PC65                 0.000000e+00 0.000000e+00
## PC66                 0.000000e+00 0.000000e+00
## PC67                 0.000000e+00 0.000000e+00
## PC69                 0.000000e+00 0.000000e+00
## PC71                 0.000000e+00 0.000000e+00
## PC72                 0.000000e+00 0.000000e+00
## PC73                 0.000000e+00 0.000000e+00
## PC76                 0.000000e+00 0.000000e+00
## PC78                 0.000000e+00 0.000000e+00
## PC79                 0.000000e+00 0.000000e+00
## PC80                 0.000000e+00 0.000000e+00
## PC81                 0.000000e+00 0.000000e+00
## PC82                 0.000000e+00 0.000000e+00
## PC83                 0.000000e+00 0.000000e+00
## PC86                 0.000000e+00 0.000000e+00
## PC89                 0.000000e+00 0.000000e+00
## PC90                 0.000000e+00 0.000000e+00
## PC91                 0.000000e+00 0.000000e+00
## PC93                 0.000000e+00 0.000000e+00
## PC94                 0.000000e+00 0.000000e+00
## PC96                 0.000000e+00 0.000000e+00
## PC97                 0.000000e+00 0.000000e+00
## PC98                 0.000000e+00 0.000000e+00
## PC104                0.000000e+00 0.000000e+00
## PC105                0.000000e+00 0.000000e+00
## PC107                0.000000e+00 0.000000e+00
## PC110                0.000000e+00 0.000000e+00
## PC112                0.000000e+00 0.000000e+00
## PC115                0.000000e+00 0.000000e+00
## PC116                0.000000e+00 0.000000e+00
## PC119                0.000000e+00 0.000000e+00
## PC121                0.000000e+00 0.000000e+00
## PC122                0.000000e+00 0.000000e+00
## PC125                0.000000e+00 0.000000e+00
## PC127                0.000000e+00 0.000000e+00
## PC129                0.000000e+00 0.000000e+00
## PC131                0.000000e+00 0.000000e+00
## PC132                0.000000e+00 0.000000e+00
## PC134                0.000000e+00 0.000000e+00
## PC135                0.000000e+00 0.000000e+00
## PC136                0.000000e+00 0.000000e+00
## PC139                0.000000e+00 0.000000e+00
## PC141                0.000000e+00 0.000000e+00
## PC143                0.000000e+00 0.000000e+00
## PC145                0.000000e+00 0.000000e+00
## PC146                0.000000e+00 0.000000e+00
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        if (grepl("pca", myparseMdlId(mdl_id)$preProc, fixed = TRUE)) {
            indepVar <- unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], ","))
            vectorizedObsMtx <- myget_vectorized_obs_df(obs_df, rsp_var = glb_rsp_var, 
                indep_vars = indepVar)
            if (!inherits(vectorizedObsMtx, "matrix")) {
                vectorizedObsMtx[, glb_rsp_var] <- as.numeric(vectorizedObsMtx[, glb_rsp_var])
                vectorizedObsMtx <- as.matrix(vectorizedObsMtx)
            }    
            rotationMtx <- glb_models_lst[[mdl_id]]$preProcess$rotation
            pcaobs_df <- as.data.frame(vectorizedObsMtx[, dimnames(rotationMtx)[[1]]] %*%
                                           rotationMtx)
            # pcaobs_df[, glb_rsp_var] <- obs_df[, glb_rsp_var]
            # pcaobs_df[, rsp_var_out] <- obs_df[, rsp_var_out]
            # pcaobs_df[, glbFeatsId]  <- obs_df[, glbFeatsId]            
            obs_df <- cbind(obs_df, pcaobs_df)
        }    
        
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        require(lazyeval)
        
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important") else
            print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId, 
            prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 146

## Loading required package: lazyeval

## [1] "Min/Max Boundaries: "
##   USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 1    3908          D                              0.13999479
## 2    4498          D                              0.08080308
## 3      48          D                              0.59038150
## 4    5858          D                              0.70569556
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 1                                  D
## 2                                  D
## 3                                  R
## 4                                  R
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 1                                  FALSE
## 2                                  FALSE
## 3                                   TRUE
## 4                                   TRUE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                 0.13999479
## 2                                 0.08080308
## 3                                 0.59038150
## 4                                 0.70569556
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                      TRUE
## 2                                      TRUE
## 3                                     FALSE
## 4                                     FALSE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.accurate
## 1                                        TRUE
## 2                                        TRUE
## 3                                       FALSE
## 4                                       FALSE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.error .label
## 1                                0.0000000   3908
## 2                                0.0000000   4498
## 3                                0.0903815     48
## 4                                0.2056956   5858
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 1    3895          R                              0.06556290
## 2    1515          R                              0.07338278
## 3     468          R                              0.07477716
## 4    1653          R                              0.08336510
## 5    1307          R                              0.08611118
## 6    3878          R                              0.09605418
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 1                                  D
## 2                                  D
## 3                                  D
## 4                                  D
## 5                                  D
## 6                                  D
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 1                                   TRUE
## 2                                   TRUE
## 3                                   TRUE
## 4                                   TRUE
## 5                                   TRUE
## 6                                   TRUE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                  0.9344371
## 2                                  0.9266172
## 3                                  0.9252228
## 4                                  0.9166349
## 5                                  0.9138888
## 6                                  0.9039458
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                     FALSE
## 2                                     FALSE
## 3                                     FALSE
## 4                                     FALSE
## 5                                     FALSE
## 6                                     FALSE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.accurate
## 1                                       FALSE
## 2                                       FALSE
## 3                                       FALSE
## 4                                       FALSE
## 5                                       FALSE
## 6                                       FALSE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.error
## 1                               -0.4344371
## 2                               -0.4266172
## 3                               -0.4252228
## 4                               -0.4166349
## 5                               -0.4138888
## 6                               -0.4039458
##     USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 104     277          R                               0.3674392
## 211    5162          R                               0.4757984
## 268    4298          D                               0.5121844
## 385    6880          D                               0.6100009
## 429     477          D                               0.6963740
## 432    1765          D                               0.7015358
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 104                                  D
## 211                                  D
## 268                                  R
## 385                                  R
## 429                                  R
## 432                                  R
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 104                                   TRUE
## 211                                   TRUE
## 268                                   TRUE
## 385                                   TRUE
## 429                                   TRUE
## 432                                   TRUE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 104                                  0.6325608
## 211                                  0.5242016
## 268                                  0.5121844
## 385                                  0.6100009
## 429                                  0.6963740
## 432                                  0.7015358
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 104                                     FALSE
## 211                                     FALSE
## 268                                     FALSE
## 385                                     FALSE
## 429                                     FALSE
## 432                                     FALSE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.accurate
## 104                                       FALSE
## 211                                       FALSE
## 268                                       FALSE
## 385                                       FALSE
## 429                                       FALSE
## 432                                       FALSE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.error
## 104                              -0.13256075
## 211                              -0.02420158
## 268                               0.01218441
## 385                               0.11000095
## 429                               0.19637398
## 432                               0.20153577
##     USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 460     892          D                               0.8034368
## 461    4859          D                               0.8062765
## 462    3978          D                               0.8089729
## 463    1309          D                               0.8106673
## 464     217          D                               0.8319108
## 465    4276          D                               0.8587556
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 460                                  R
## 461                                  R
## 462                                  R
## 463                                  R
## 464                                  R
## 465                                  R
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 460                                   TRUE
## 461                                   TRUE
## 462                                   TRUE
## 463                                   TRUE
## 464                                   TRUE
## 465                                   TRUE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 460                                  0.8034368
## 461                                  0.8062765
## 462                                  0.8089729
## 463                                  0.8106673
## 464                                  0.8319108
## 465                                  0.8587556
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 460                                     FALSE
## 461                                     FALSE
## 462                                     FALSE
## 463                                     FALSE
## 464                                     FALSE
## 465                                     FALSE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.accurate
## 460                                       FALSE
## 461                                       FALSE
## 462                                       FALSE
## 463                                       FALSE
## 464                                       FALSE
## 465                                       FALSE
##     Party.fctr.RFE.X.zv.pca.rcv.glmnet.error
## 460                                0.3034368
## 461                                0.3062765
## 462                                0.3089729
## 463                                0.3106673
## 464                                0.3319108
## 465                                0.3587556

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##     Q109244.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## No            No    498   1961    622      0.4403773      0.4466368
## NA            NA    438   1746    547      0.3920952      0.3928251
## Yes          Yes    179    746    223      0.1675275      0.1605381
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## No       0.4468391        883.9618        0.4507709   1961       239.90734
## NA       0.3929598        821.7109        0.4706248   1746       209.08425
## Yes      0.1602011        200.4801        0.2687400    746        85.28436
##     err.abs.OOB.mean
## No         0.4817416
## NA         0.4773613
## Yes        0.4764489
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      1115.000000      4453.000000      1392.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000      1906.152762         1.190136 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      4453.000000       534.275955         1.435552
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 334.101  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##        label step_major step_minor label_minor     bgn    end elapsed
## 2 fit.models          1          1           1 319.665 334.11  14.445
## 3 fit.models          1          2           2 334.111     NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##               label step_major step_minor label_minor     bgn     end
## 3        fit.models          1          2           2 334.111 339.957
## 4 fit.data.training          2          0           0 339.957      NA
##   elapsed
## 3   5.846
## 4      NA

Step 2.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var]))) {    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
    if (myparseMdlId(glbMdlSelId)$family == "RFE.X") {
        indepVar <- mygetIndepVar(glb_feats_df)
        trnRFEResults <- 
            myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
        if (!isTRUE(all.equal(sort(predictors(trnRFEResults)),
                              sort(predictors(glbRFEResults))))) {
            print("Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):")
            print(setdiff(predictors(trnRFEResults), predictors(glbRFEResults)))
            print("Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):")
            print(setdiff(predictors(glbRFEResults), predictors(trnRFEResults)))
        }
    }

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(trnRFEResults))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        # indepVar <- myextract_actual_feats(predictors(trnRFEResults))
        indepVar <- myextract_actual_feats(predictors(glbRFEResults))        
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    # if (!is.null(glbMdlPreprocMethods) &&
    #     ((match_pos <- regexpr(gsub(".", "\\.", 
    #                                 paste(glbMdlPreprocMethods, collapse = "|"),
    #                                fixed = TRUE), glbMdlSelId)) != -1))
    #     ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
    #                             match_pos + attr(match_pos, "match.length") - 1) else
    #     ths_preProcess <- NULL   
    
    # mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
    #                                "Final.Ensemble", "Final")
    mdl_id_pfx <- paste("Final", myparseMdlId(glbMdlSelId)$family, sep = ".")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = myparseMdlId(glbMdlSelId)$preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}
## +(rfe) fit Fold1.Rep1 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep1 size: 247 
## +(rfe) imp Fold1.Rep1 
## -(rfe) imp Fold1.Rep1 
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## Warning in lda.default(x, grouping, ...): variables are collinear
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## +(rfe) imp Fold2.Rep1 
## -(rfe) imp Fold2.Rep1 
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## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep1 size: 247 
## +(rfe) imp Fold3.Rep1 
## -(rfe) imp Fold3.Rep1 
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## +(rfe) fit Fold1.Rep2 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep2 size: 247 
## +(rfe) imp Fold1.Rep2 
## -(rfe) imp Fold1.Rep2 
## +(rfe) fit Fold1.Rep2 size: 128 
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## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep2 size: 247 
## +(rfe) imp Fold2.Rep2 
## -(rfe) imp Fold2.Rep2 
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## +(rfe) fit Fold3.Rep2 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep2 size: 247 
## +(rfe) imp Fold3.Rep2 
## -(rfe) imp Fold3.Rep2 
## +(rfe) fit Fold3.Rep2 size: 128 
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## +(rfe) fit Fold1.Rep3 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold1.Rep3 size: 247 
## +(rfe) imp Fold1.Rep3 
## -(rfe) imp Fold1.Rep3 
## +(rfe) fit Fold1.Rep3 size: 128 
## -(rfe) fit Fold1.Rep3 size: 128 
## +(rfe) fit Fold1.Rep3 size:  96 
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## +(rfe) fit Fold1.Rep3 size:   8 
## -(rfe) fit Fold1.Rep3 size:   8 
## +(rfe) fit Fold2.Rep3 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold2.Rep3 size: 247 
## +(rfe) imp Fold2.Rep3 
## -(rfe) imp Fold2.Rep3 
## +(rfe) fit Fold2.Rep3 size: 128 
## -(rfe) fit Fold2.Rep3 size: 128 
## +(rfe) fit Fold2.Rep3 size:  96 
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## +(rfe) fit Fold2.Rep3 size:   8 
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## +(rfe) fit Fold3.Rep3 size: 247
## Warning in lda.default(x, grouping, ...): variables are collinear
## -(rfe) fit Fold3.Rep3 size: 247 
## +(rfe) imp Fold3.Rep3 
## -(rfe) imp Fold3.Rep3 
## +(rfe) fit Fold3.Rep3 size: 128 
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## -(rfe) fit Fold3.Rep3 size:  52 
## +(rfe) fit Fold3.Rep3 size:  51 
## -(rfe) fit Fold3.Rep3 size:  51 
## +(rfe) fit Fold3.Rep3 size:  50 
## -(rfe) fit Fold3.Rep3 size:  50 
## +(rfe) fit Fold3.Rep3 size:  49 
## -(rfe) fit Fold3.Rep3 size:  49 
## +(rfe) fit Fold3.Rep3 size:  48 
## -(rfe) fit Fold3.Rep3 size:  48 
## +(rfe) fit Fold3.Rep3 size:  46 
## -(rfe) fit Fold3.Rep3 size:  46 
## +(rfe) fit Fold3.Rep3 size:  44 
## -(rfe) fit Fold3.Rep3 size:  44 
## +(rfe) fit Fold3.Rep3 size:  40 
## -(rfe) fit Fold3.Rep3 size:  40 
## +(rfe) fit Fold3.Rep3 size:  32 
## -(rfe) fit Fold3.Rep3 size:  32 
## +(rfe) fit Fold3.Rep3 size:  16 
## -(rfe) fit Fold3.Rep3 size:  16 
## +(rfe) fit Fold3.Rep3 size:   8 
## -(rfe) fit Fold3.Rep3 size:   8 
## 
## Recursive feature selection
## 
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times) 
## 
## Resampling performance over subset size:
## 
##  Variables Accuracy  Kappa AccuracySD KappaSD Selected
##          8   0.6061 0.2027   0.015046 0.02955         
##         16   0.6066 0.2021   0.016324 0.03294         
##         32   0.6103 0.2102   0.014275 0.02881         
##         40   0.6142 0.2185   0.019295 0.03900         
##         44   0.6151 0.2206   0.017705 0.03573         
##         46   0.6147 0.2198   0.015989 0.03232         
##         48   0.6153 0.2210   0.018660 0.03771         
##         49   0.6164 0.2232   0.019028 0.03847        *
##         50   0.6154 0.2214   0.018224 0.03687         
##         51   0.6152 0.2209   0.017556 0.03546         
##         52   0.6139 0.2183   0.017950 0.03626         
##         56   0.6130 0.2167   0.014393 0.02893         
##         64   0.6149 0.2206   0.016610 0.03353         
##         96   0.6124 0.2169   0.012169 0.02474         
##        128   0.6100 0.2130   0.009981 0.02037         
##        247   0.6074 0.2110   0.008634 0.01737         
## 
## The top 5 variables (out of 49):
##    Q109244.fctrNo, Q115611.fctrYes, Q113181.fctrYes, Gender.fctrM, Q98197.fctrYes
## 
##  [1] "Q109244.fctrNo"                  "Q115611.fctrYes"                
##  [3] "Q113181.fctrYes"                 "Gender.fctrM"                   
##  [5] "Q98197.fctrYes"                  "Q101163.fctrDad"                
##  [7] "Hhold.fctrMKy"                   "Q120472.fctrScience"            
##  [9] "Q120379.fctrNo"                  "Q105840.fctrNo"                 
## [11] "Q98869.fctrYes"                  "Q99480.fctrYes"                 
## [13] "Q106272.fctrYes"                 "Q109244.fctrNo:.clusterid.fctr3"
## [15] "Q119851.fctrNo"                  "Q116881.fctrRight"              
## [17] "Q110740.fctrPC"                  "Q115899.fctrMe"                 
## [19] "Q106042.fctrNo"                  "Income.fctr.Q"                  
## [21] "Q120014.fctrYes"                 "Q108855.fctrYes!"               
## [23] "Q101596.fctrYes"                 "Q121699.fctrNo"                 
## [25] "Q121699.fctrYes"                 "Q102089.fctrOwn"                
## [27] "Q107869.fctrYes"                 "Q118232.fctrPr"                 
## [29] "Q112478.fctrNo"                  "Edn.fctr^7"                     
## [31] "Q120650.fctrYes"                 "Q100680.fctrNo"                 
## [33] "Q120012.fctrNo"                  "Q106389.fctrNo"                 
## [35] "Q115390.fctrNo"                  "Q118237.fctrNo"                 
## [37] "Q118892.fctrNo"                  "Q121011.fctrNo"                 
## [39] "Q111220.fctrNo"                  "Q115195.fctrNo"                 
## [41] "Q108342.fctrIn-person"           "Q116448.fctrNo"                 
## [43] "Q108617.fctrNo"                  "Q116953.fctrNo"                 
## [45] "Q124122.fctrNo"                  "Q119650.fctrGiving"             
## [47] "Q122771.fctrPt"                  "Q106388.fctrYes"                
## [49] "Q120194.fctrTry first"

## [1] "Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):"
## character(0)
## [1] "Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):"
##  [1] "Edn.fctr^6"                      "Q96024.fctrYes"                 
##  [3] "Q123621.fctrYes"                 "Q113583.fctrTalk"               
##  [5] "Q122770.fctrYes"                 "Q121700.fctrNo"                 
##  [7] "Q114152.fctrYes"                 "Q106997.fctrGr"                 
##  [9] "Q98078.fctrNo"                   "Q112270.fctrYes"                
## [11] "Q111848.fctrNo"                  "Q100689.fctrNo"                 
## [13] "Income.fctr^4"                   "Q118233.fctrNo"                 
## [15] "Q99982.fctrNope"                 "Q106993.fctrYes"                
## [17] "Q102906.fctrNo"                  "Q123464.fctrNo"                 
## [19] "Q116797.fctrNo"                  "Q104996.fctrNo"                 
## [21] "Q109367.fctrNo"                  "Q99716.fctrNo"                  
## [23] "Q117186.fctrHot headed"          "Q102674.fctrNo"                 
## [25] "Q118117.fctrYes"                 "Q122769.fctrYes"                
## [27] "Edn.fctr.C"                      "Q108754.fctrNo"                 
## [29] "Q116441.fctrYes"                 "YOB.Age.fctr^7"                 
## [31] "Q105655.fctrYes"                 "Q108856.fctrSocialize"          
## [33] "YOB.Age.fctr^8"                  "Q120978.fctrNo"                 
## [35] "Q101162.fctrPessimist"           "Q122769.fctrNo"                 
## [37] "Q119334.fctrNo"                  "Q102289.fctrYes"                
## [39] "Q111580.fctrSupportive"          "Q112512.fctrYes"                
## [41] "Q99581.fctrNo"                   "Q122120.fctrNo"                 
## [43] "Income.fctr.L"                   "Q116197.fctrP.M."               
## [45] "Q114386.fctrMysterious"          "Q117193.fctrStandard hours"     
## [47] "Q114961.fctrNo"                  "Q115777.fctrStart"              
## [49] "YOB.Age.fctr(30,35]:YOB.Age.dff" "Q100010.fctrYes"                
## [51] "Q122120.fctrYes"                 "Q113584.fctrTechnology"         
## [53] "Q119334.fctrYes"                 "Q98578.fctrYes"                 
## [55] "Q102687.fctrNo"                  "Q100562.fctrNo"                 
## [57] "Q116601.fctrNo"                  "Q116197.fctrA.M."               
## [59] "Q103293.fctrYes"                 "Q108950.fctrCautious"           
## [61] "Q98578.fctrNo"                   "Q103293.fctrNo"                 
## [63] "Q115602.fctrYes"                 "YOB.Age.fctr(40,50]:YOB.Age.dff"
## [65] "Q107491.fctrNo"                  "Q114748.fctrYes"                
## [67] "Q115777.fctrEnd"                 "Q114517.fctrYes"                
## [69] "Edn.fctr^5"                      "Q108856.fctrSpace"              
## [71] "YOB.Age.fctr^5"                  "Q115610.fctrYes"                
## [73] "Income.fctr.C"                   "Q109244.fctrNo:.clusterid.fctr2"
## [75] "Q124742.fctrYes"                 "Q108343.fctrNo"                 
## [77] "Income.fctr^5"                   "Q105655.fctrNo"                 
## [79] "Q111580.fctrDemanding"           "Q115610.fctrNo"                 
## [81] "Q108343.fctrYes"                 "Q113992.fctrNo"                 
## [1] "\nmyfit_mdl: enter: 0.001000 secs"
## [1] "fitting model: Final.RFE.X#zv.pca#rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,.clusterid.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,YOB.Age.dff,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr"
## [1] "myfit_mdl: setup complete: 0.726000 secs"
## + Fold1.Rep1: alpha=0.100, lambda=0.04062 
## - Fold1.Rep1: alpha=0.100, lambda=0.04062 
## + Fold1.Rep1: alpha=0.325, lambda=0.04062 
## - Fold1.Rep1: alpha=0.325, lambda=0.04062 
## + Fold1.Rep1: alpha=0.550, lambda=0.04062 
## - Fold1.Rep1: alpha=0.550, lambda=0.04062 
## + Fold1.Rep1: alpha=0.775, lambda=0.04062 
## - Fold1.Rep1: alpha=0.775, lambda=0.04062 
## + Fold1.Rep1: alpha=1.000, lambda=0.04062 
## - Fold1.Rep1: alpha=1.000, lambda=0.04062 
## + Fold2.Rep1: alpha=0.100, lambda=0.04062 
## - Fold2.Rep1: alpha=0.100, lambda=0.04062 
## + Fold2.Rep1: alpha=0.325, lambda=0.04062 
## - Fold2.Rep1: alpha=0.325, lambda=0.04062 
## + Fold2.Rep1: alpha=0.550, lambda=0.04062 
## - Fold2.Rep1: alpha=0.550, lambda=0.04062 
## + Fold2.Rep1: alpha=0.775, lambda=0.04062 
## - Fold2.Rep1: alpha=0.775, lambda=0.04062 
## + Fold2.Rep1: alpha=1.000, lambda=0.04062 
## - Fold2.Rep1: alpha=1.000, lambda=0.04062 
## + Fold3.Rep1: alpha=0.100, lambda=0.04062 
## - Fold3.Rep1: alpha=0.100, lambda=0.04062 
## + Fold3.Rep1: alpha=0.325, lambda=0.04062 
## - Fold3.Rep1: alpha=0.325, lambda=0.04062 
## + Fold3.Rep1: alpha=0.550, lambda=0.04062 
## - Fold3.Rep1: alpha=0.550, lambda=0.04062 
## + Fold3.Rep1: alpha=0.775, lambda=0.04062 
## - Fold3.Rep1: alpha=0.775, lambda=0.04062 
## + Fold3.Rep1: alpha=1.000, lambda=0.04062 
## - Fold3.Rep1: alpha=1.000, lambda=0.04062 
## + Fold1.Rep2: alpha=0.100, lambda=0.04062 
## - Fold1.Rep2: alpha=0.100, lambda=0.04062 
## + Fold1.Rep2: alpha=0.325, lambda=0.04062 
## - Fold1.Rep2: alpha=0.325, lambda=0.04062 
## + Fold1.Rep2: alpha=0.550, lambda=0.04062 
## - Fold1.Rep2: alpha=0.550, lambda=0.04062 
## + Fold1.Rep2: alpha=0.775, lambda=0.04062 
## - Fold1.Rep2: alpha=0.775, lambda=0.04062 
## + Fold1.Rep2: alpha=1.000, lambda=0.04062 
## - Fold1.Rep2: alpha=1.000, lambda=0.04062 
## + Fold2.Rep2: alpha=0.100, lambda=0.04062 
## - Fold2.Rep2: alpha=0.100, lambda=0.04062 
## + Fold2.Rep2: alpha=0.325, lambda=0.04062 
## - Fold2.Rep2: alpha=0.325, lambda=0.04062 
## + Fold2.Rep2: alpha=0.550, lambda=0.04062 
## - Fold2.Rep2: alpha=0.550, lambda=0.04062 
## + Fold2.Rep2: alpha=0.775, lambda=0.04062 
## - Fold2.Rep2: alpha=0.775, lambda=0.04062 
## + Fold2.Rep2: alpha=1.000, lambda=0.04062 
## - Fold2.Rep2: alpha=1.000, lambda=0.04062 
## + Fold3.Rep2: alpha=0.100, lambda=0.04062 
## - Fold3.Rep2: alpha=0.100, lambda=0.04062 
## + Fold3.Rep2: alpha=0.325, lambda=0.04062 
## - Fold3.Rep2: alpha=0.325, lambda=0.04062 
## + Fold3.Rep2: alpha=0.550, lambda=0.04062 
## - Fold3.Rep2: alpha=0.550, lambda=0.04062 
## + Fold3.Rep2: alpha=0.775, lambda=0.04062 
## - Fold3.Rep2: alpha=0.775, lambda=0.04062 
## + Fold3.Rep2: alpha=1.000, lambda=0.04062 
## - Fold3.Rep2: alpha=1.000, lambda=0.04062 
## + Fold1.Rep3: alpha=0.100, lambda=0.04062 
## - Fold1.Rep3: alpha=0.100, lambda=0.04062 
## + Fold1.Rep3: alpha=0.325, lambda=0.04062 
## - Fold1.Rep3: alpha=0.325, lambda=0.04062 
## + Fold1.Rep3: alpha=0.550, lambda=0.04062 
## - Fold1.Rep3: alpha=0.550, lambda=0.04062 
## + Fold1.Rep3: alpha=0.775, lambda=0.04062 
## - Fold1.Rep3: alpha=0.775, lambda=0.04062 
## + Fold1.Rep3: alpha=1.000, lambda=0.04062 
## - Fold1.Rep3: alpha=1.000, lambda=0.04062 
## + Fold2.Rep3: alpha=0.100, lambda=0.04062 
## - Fold2.Rep3: alpha=0.100, lambda=0.04062 
## + Fold2.Rep3: alpha=0.325, lambda=0.04062 
## - Fold2.Rep3: alpha=0.325, lambda=0.04062 
## + Fold2.Rep3: alpha=0.550, lambda=0.04062 
## - Fold2.Rep3: alpha=0.550, lambda=0.04062 
## + Fold2.Rep3: alpha=0.775, lambda=0.04062 
## - Fold2.Rep3: alpha=0.775, lambda=0.04062 
## + Fold2.Rep3: alpha=1.000, lambda=0.04062 
## - Fold2.Rep3: alpha=1.000, lambda=0.04062 
## + Fold3.Rep3: alpha=0.100, lambda=0.04062 
## - Fold3.Rep3: alpha=0.100, lambda=0.04062 
## + Fold3.Rep3: alpha=0.325, lambda=0.04062 
## - Fold3.Rep3: alpha=0.325, lambda=0.04062 
## + Fold3.Rep3: alpha=0.550, lambda=0.04062 
## - Fold3.Rep3: alpha=0.550, lambda=0.04062 
## + Fold3.Rep3: alpha=0.775, lambda=0.04062 
## - Fold3.Rep3: alpha=0.775, lambda=0.04062 
## + Fold3.Rep3: alpha=1.000, lambda=0.04062 
## - Fold3.Rep3: alpha=1.000, lambda=0.04062 
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00875 on full training set
## [1] "myfit_mdl: train complete: 94.424000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            64   -none-     numeric  
## beta        9152   dgCMatrix  S4       
## df            64   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        64   -none-     numeric  
## dev.ratio     64   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames       143   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##   (Intercept)           PC2           PC3           PC4           PC5 
## -0.1327006681  0.0210662656 -0.0877888193  0.0417899970 -0.1042532934 
##           PC6           PC7           PC9          PC10          PC12 
## -0.0302547417  0.2100782515  0.0228261127  0.1055386942  0.0909002772 
##          PC14          PC15          PC16          PC20          PC23 
## -0.0359218384 -0.0866939426 -0.0284090500  0.0021295865  0.0400977194 
##          PC27          PC28          PC31          PC37          PC38 
## -0.0237693776  0.0202106385 -0.0500965637  0.0031020807 -0.0401755947 
##          PC42          PC56          PC61          PC62          PC66 
## -0.0348872995 -0.0408258157  0.0126715178 -0.0130830641 -0.0214020261 
##          PC71          PC72          PC74          PC77          PC86 
## -0.0053840964  0.0059595427  0.0025397394 -0.0458931313 -0.0343981132 
##          PC88          PC91          PC92          PC94          PC97 
##  0.0406823986  0.0077176109 -0.0904246271  0.0176968566 -0.0609112378 
##         PC100         PC105         PC106         PC107         PC108 
## -0.0053485006 -0.0006374331  0.0091673919  0.0172500586  0.0122925023 
##         PC110         PC113         PC120         PC122         PC128 
## -0.0026630849 -0.0435850118 -0.0241128550  0.0185458332  0.0105287421 
##         PC133         PC139 
##  0.0141463690 -0.0176353383 
## [1] "max lambda < lambdaOpt:"
##   (Intercept)           PC2           PC3           PC4           PC5 
## -0.1338208543  0.0224823418 -0.0897897766  0.0437228080 -0.1065667129 
##           PC6           PC7           PC9          PC10          PC12 
## -0.0322826064  0.2131293784  0.0251385795  0.1083202536  0.0939545933 
##          PC14          PC15          PC16          PC19          PC20 
## -0.0386115724 -0.0898848758 -0.0312137507 -0.0020613180  0.0050243113 
##          PC23          PC27          PC28          PC31          PC37 
##  0.0430832242 -0.0267243679  0.0231676453 -0.0532371992  0.0061978493 
##          PC38          PC42          PC43          PC44          PC54 
## -0.0433863135 -0.0381792566  0.0022232839  0.0001705535 -0.0008040441 
##          PC55          PC56          PC61          PC62          PC66 
## -0.0003430840 -0.0443717115  0.0160624914 -0.0165569057 -0.0248944454 
##          PC71          PC72          PC74          PC77          PC83 
## -0.0087704273  0.0095943642  0.0059444800 -0.0494294437 -0.0028986573 
##          PC86          PC88          PC91          PC92          PC93 
## -0.0380823412  0.0445153738  0.0115378223 -0.0944285261 -0.0017854522 
##          PC94          PC97         PC100         PC105         PC106 
##  0.0215306095 -0.0650011868 -0.0089615381 -0.0045342285  0.0130677546 
##         PC107         PC108         PC110         PC113         PC120 
##  0.0211373785  0.0162565151 -0.0066331818 -0.0478273643 -0.0283560147 
##         PC122         PC128         PC133         PC139         PC142 
##  0.0229407357  0.0148733907  0.0189373916 -0.0231024419  0.0055967838 
## [1] "myfit_mdl: train diagnostics complete: 100.545000 secs"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess

## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk

##          Prediction
## Reference    D    R
##         D 2121  830
##         R 1108 1509
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.519397e-01   2.971401e-01   6.392607e-01   6.644592e-01   5.299928e-01 
## AccuracyPValue  McnemarPValue 
##   6.144311e-76   3.129872e-10 
## [1] "myfit_mdl: predict complete: 134.421000 secs"
##                              id
## 1 Final.RFE.X#zv.pca#rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## 1 Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,.clusterid.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,YOB.Age.dff,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      93.59                 2.123
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6476769    0.7187394    0.5766144       0.7037082
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.6089588        0.6272737
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6392607             0.6644592     0.2475136
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.008937507      0.01753324
## [1] "myfit_mdl: exit: 134.438000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##               label step_major step_minor label_minor     bgn     end
## 4 fit.data.training          2          0           0 339.957 517.207
## 5 fit.data.training          2          1           1 517.208      NA
##   elapsed
## 4 177.251
## 5      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.5
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##       RFE.X.zv.pca.rcv.glmnet.imp Final.RFE.X.zv.pca.rcv.glmnet.imp
## PC7                  0.000000e+00                      100.00000000
## PC10                 6.660214e+01                       50.56857563
## PC5                  7.995721e+01                       49.83764917
## PC92                 6.234515e+00                       43.75600909
## PC12                 1.279928e-01                       43.72906314
## PC3                  4.474537e+01                       41.98091808
## PC15                 6.321726e+01                       41.77916319
## PC97                 0.000000e+00                       29.84358506
## PC31                 0.000000e+00                       24.48580379
## PC77                 8.954286e+00                       22.60589561
## PC113                2.752156e+00                       21.70310070
## PC4                  4.495763e+00                       20.24378933
## PC88                 2.477208e+01                       20.22414706
## PC56                 0.000000e+00                       20.21582023
## PC38                 0.000000e+00                       19.82002416
## PC23                 1.488607e+01                       19.72359791
## PC14                 4.398423e+01                       17.67354338
## PC42                 0.000000e+00                       17.34459014
## PC86                 0.000000e+00                       17.21752030
## PC6                  8.539962e+00                       14.82241300
## PC16                 2.054784e+01                       14.15671362
## PC120                7.951659e-03                       12.50922508
## PC27                 6.583676e+00                       12.00582798
## PC9                  5.584978e+01                       11.39025458
## PC66                 0.000000e+00                       11.03040295
## PC28                 0.000000e+00                       10.32604668
## PC2                  1.110115e+01                       10.32187287
## PC122                0.000000e+00                        9.92086158
## PC139                0.000000e+00                        9.77496851
## PC94                 0.000000e+00                        9.37136273
## PC107                0.000000e+00                        9.17458911
## PC133                8.569951e-02                        7.94851125
## PC62                 0.000000e+00                        7.09755133
## PC61                 0.000000e+00                        6.88128256
## PC108                6.523922e+00                        6.85411474
## PC128                2.424400e+01                        6.12215252
## PC106                6.709574e+00                        5.36168397
## PC91                 0.000000e+00                        4.65591574
## PC72                 0.000000e+00                        3.77670850
## PC100                1.448174e+01                        3.48242493
## PC71                 0.000000e+00                        3.43918050
## PC110                0.000000e+00                        2.30904330
## PC37                 2.029163e-02                        2.28472436
## PC74                 5.526187e+00                        2.10104845
## PC20                 6.471282e+00                        1.77229191
## PC142                1.751430e+01                        1.48251276
## PC105                0.000000e+00                        1.33318291
## PC83                 0.000000e+00                        0.76781535
## PC43                 0.000000e+00                        0.58891802
## PC19                 2.341047e+01                        0.54601541
## PC93                 0.000000e+00                        0.47294227
## PC54                 5.627894e+00                        0.21298046
## PC55                 0.000000e+00                        0.09087835
## PC44                 4.657604e+00                        0.04517734
## PC1                  1.487771e+00                        0.00000000
## PC101                1.257034e+01                        0.00000000
## PC102                3.345513e+00                        0.00000000
## PC103                2.182427e+00                        0.00000000
## PC104                0.000000e+00                        0.00000000
## PC109                4.849634e+00                        0.00000000
## PC11                 1.060646e+01                        0.00000000
## PC111                5.951208e+00                        0.00000000
## PC112                0.000000e+00                        0.00000000
## PC114                1.650518e+01                        0.00000000
## PC115                0.000000e+00                        0.00000000
## PC116                0.000000e+00                        0.00000000
## PC117                5.778034e+00                        0.00000000
## PC118                4.902623e+00                        0.00000000
## PC119                0.000000e+00                        0.00000000
## PC121                0.000000e+00                        0.00000000
## PC123                4.889254e-01                        0.00000000
## PC124                2.213194e+01                        0.00000000
## PC125                0.000000e+00                        0.00000000
## PC126                1.295801e+01                        0.00000000
## PC127                0.000000e+00                        0.00000000
## PC129                0.000000e+00                        0.00000000
## PC13                 0.000000e+00                        0.00000000
## PC130                2.081387e+00                        0.00000000
## PC131                0.000000e+00                        0.00000000
## PC132                0.000000e+00                        0.00000000
## PC134                0.000000e+00                        0.00000000
## PC135                0.000000e+00                        0.00000000
## PC136                0.000000e+00                        0.00000000
## PC137                7.388924e-02                        0.00000000
## PC138                1.321459e+01                        0.00000000
## PC140                5.171006e+00                        0.00000000
## PC141                0.000000e+00                        0.00000000
## PC143                0.000000e+00                        0.00000000
## PC17                 8.207663e+00                        0.00000000
## PC18                 0.000000e+00                        0.00000000
## PC21                 0.000000e+00                        0.00000000
## PC22                 1.452383e+01                        0.00000000
## PC24                 0.000000e+00                        0.00000000
## PC25                 2.371905e+01                        0.00000000
## PC26                 5.050796e+01                        0.00000000
## PC29                 0.000000e+00                        0.00000000
## PC30                 2.569378e+01                        0.00000000
## PC32                 0.000000e+00                        0.00000000
## PC33                 1.735816e+00                        0.00000000
## PC34                 0.000000e+00                        0.00000000
## PC35                 4.402217e+00                        0.00000000
## PC36                 0.000000e+00                        0.00000000
## PC39                 0.000000e+00                        0.00000000
## PC40                 1.496730e+01                        0.00000000
## PC41                 0.000000e+00                        0.00000000
## PC45                 1.271547e+01                        0.00000000
## PC46                 2.206416e-01                        0.00000000
## PC47                 6.727046e+00                        0.00000000
## PC48                 0.000000e+00                        0.00000000
## PC49                 3.217595e+00                        0.00000000
## PC50                 0.000000e+00                        0.00000000
## PC51                 1.903814e+00                        0.00000000
## PC52                 2.128227e+01                        0.00000000
## PC53                 0.000000e+00                        0.00000000
## PC57                 3.105066e+01                        0.00000000
## PC58                 0.000000e+00                        0.00000000
## PC59                 0.000000e+00                        0.00000000
## PC60                 0.000000e+00                        0.00000000
## PC63                 0.000000e+00                        0.00000000
## PC64                 0.000000e+00                        0.00000000
## PC65                 0.000000e+00                        0.00000000
## PC67                 0.000000e+00                        0.00000000
## PC68                 2.684775e+01                        0.00000000
## PC69                 0.000000e+00                        0.00000000
## PC70                 7.297321e+00                        0.00000000
## PC73                 0.000000e+00                        0.00000000
## PC75                 1.133188e+00                        0.00000000
## PC76                 0.000000e+00                        0.00000000
## PC78                 0.000000e+00                        0.00000000
## PC79                 0.000000e+00                        0.00000000
## PC8                  1.000000e+02                        0.00000000
## PC80                 0.000000e+00                        0.00000000
## PC81                 0.000000e+00                        0.00000000
## PC82                 0.000000e+00                        0.00000000
## PC84                 3.861871e+00                        0.00000000
## PC85                 5.090498e+00                        0.00000000
## PC87                 5.856015e+00                        0.00000000
## PC89                 0.000000e+00                        0.00000000
## PC90                 0.000000e+00                        0.00000000
## PC95                 6.273683e+00                        0.00000000
## PC96                 0.000000e+00                        0.00000000
## PC98                 0.000000e+00                        0.00000000
## PC99                 3.645912e-01                        0.00000000
## PC144                5.666493e+00                                NA
## PC145                0.000000e+00                                NA
## PC146                0.000000e+00                                NA
##                imp
## PC7   100.00000000
## PC10   50.56857563
## PC5    49.83764917
## PC92   43.75600909
## PC12   43.72906314
## PC3    41.98091808
## PC15   41.77916319
## PC97   29.84358506
## PC31   24.48580379
## PC77   22.60589561
## PC113  21.70310070
## PC4    20.24378933
## PC88   20.22414706
## PC56   20.21582023
## PC38   19.82002416
## PC23   19.72359791
## PC14   17.67354338
## PC42   17.34459014
## PC86   17.21752030
## PC6    14.82241300
## PC16   14.15671362
## PC120  12.50922508
## PC27   12.00582798
## PC9    11.39025458
## PC66   11.03040295
## PC28   10.32604668
## PC2    10.32187287
## PC122   9.92086158
## PC139   9.77496851
## PC94    9.37136273
## PC107   9.17458911
## PC133   7.94851125
## PC62    7.09755133
## PC61    6.88128256
## PC108   6.85411474
## PC128   6.12215252
## PC106   5.36168397
## PC91    4.65591574
## PC72    3.77670850
## PC100   3.48242493
## PC71    3.43918050
## PC110   2.30904330
## PC37    2.28472436
## PC74    2.10104845
## PC20    1.77229191
## PC142   1.48251276
## PC105   1.33318291
## PC83    0.76781535
## PC43    0.58891802
## PC19    0.54601541
## PC93    0.47294227
## PC54    0.21298046
## PC55    0.09087835
## PC44    0.04517734
## PC1     0.00000000
## PC101   0.00000000
## PC102   0.00000000
## PC103   0.00000000
## PC104   0.00000000
## PC109   0.00000000
## PC11    0.00000000
## PC111   0.00000000
## PC112   0.00000000
## PC114   0.00000000
## PC115   0.00000000
## PC116   0.00000000
## PC117   0.00000000
## PC118   0.00000000
## PC119   0.00000000
## PC121   0.00000000
## PC123   0.00000000
## PC124   0.00000000
## PC125   0.00000000
## PC126   0.00000000
## PC127   0.00000000
## PC129   0.00000000
## PC13    0.00000000
## PC130   0.00000000
## PC131   0.00000000
## PC132   0.00000000
## PC134   0.00000000
## PC135   0.00000000
## PC136   0.00000000
## PC137   0.00000000
## PC138   0.00000000
## PC140   0.00000000
## PC141   0.00000000
## PC143   0.00000000
## PC17    0.00000000
## PC18    0.00000000
## PC21    0.00000000
## PC22    0.00000000
## PC24    0.00000000
## PC25    0.00000000
## PC26    0.00000000
## PC29    0.00000000
## PC30    0.00000000
## PC32    0.00000000
## PC33    0.00000000
## PC34    0.00000000
## PC35    0.00000000
## PC36    0.00000000
## PC39    0.00000000
## PC40    0.00000000
## PC41    0.00000000
## PC45    0.00000000
## PC46    0.00000000
## PC47    0.00000000
## PC48    0.00000000
## PC49    0.00000000
## PC50    0.00000000
## PC51    0.00000000
## PC52    0.00000000
## PC53    0.00000000
## PC57    0.00000000
## PC58    0.00000000
## PC59    0.00000000
## PC60    0.00000000
## PC63    0.00000000
## PC64    0.00000000
## PC65    0.00000000
## PC67    0.00000000
## PC68    0.00000000
## PC69    0.00000000
## PC70    0.00000000
## PC73    0.00000000
## PC75    0.00000000
## PC76    0.00000000
## PC78    0.00000000
## PC79    0.00000000
## PC8     0.00000000
## PC80    0.00000000
## PC81    0.00000000
## PC82    0.00000000
## PC84    0.00000000
## PC85    0.00000000
## PC87    0.00000000
## PC89    0.00000000
## PC90    0.00000000
## PC95    0.00000000
## PC96    0.00000000
## PC98    0.00000000
## PC99    0.00000000
## PC144           NA
## PC145           NA
## PC146           NA
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 143

## [1] "Min/Max Boundaries: "
##   USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 1    1397          R                                      NA
## 2    3075          R                                      NA
## 3    6112          R                               0.4561332
## 4     118          R                               0.7204766
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 1                               <NA>
## 2                               <NA>
## 3                                  D
## 4                                  R
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 1                                     NA
## 2                                     NA
## 3                                   TRUE
## 4                                  FALSE
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                         NA
## 2                                         NA
## 3                                  0.5438668
## 4                                  0.2795234
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                        NA
## 2                                        NA
## 3                                     FALSE
## 4                                      TRUE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.prob
## 1                                     0.1366892
## 2                                     0.3074388
## 3                                     0.4867866
## 4                                     0.6803799
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet
## 1                                        D
## 2                                        D
## 3                                        D
## 4                                        R
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err
## 1                                         TRUE
## 2                                         TRUE
## 3                                         TRUE
## 4                                        FALSE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                        0.8633108
## 2                                        0.6925612
## 3                                        0.5132134
## 4                                        0.3196201
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                           FALSE
## 2                                           FALSE
## 3                                           FALSE
## 4                                            TRUE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.accurate
## 1                                             FALSE
## 2                                             FALSE
## 3                                             FALSE
## 4                                              TRUE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.error .label
## 1                                    -0.36331085   1397
## 2                                    -0.19256116   3075
## 3                                    -0.01321344   6112
## 4                                     0.00000000    118
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 1    3895          R                                      NA
## 2     626          R                              0.07256962
## 3    2226          R                              0.10893408
## 4    2590          R                              0.09674309
## 5    1397          R                                      NA
## 6     468          R                                      NA
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 1                               <NA>
## 2                                  D
## 3                                  D
## 4                                  D
## 5                               <NA>
## 6                               <NA>
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 1                                     NA
## 2                                   TRUE
## 3                                   TRUE
## 4                                   TRUE
## 5                                     NA
## 6                                     NA
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                         NA
## 2                                  0.9274304
## 3                                  0.8910659
## 4                                  0.9032569
## 5                                         NA
## 6                                         NA
##   Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                        NA
## 2                                     FALSE
## 3                                     FALSE
## 4                                     FALSE
## 5                                        NA
## 6                                        NA
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.prob
## 1                                     0.1081122
## 2                                     0.1194254
## 3                                     0.1251642
## 4                                     0.1347899
## 5                                     0.1366892
## 6                                     0.1406591
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet
## 1                                        D
## 2                                        D
## 3                                        D
## 4                                        D
## 5                                        D
## 6                                        D
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err
## 1                                         TRUE
## 2                                         TRUE
## 3                                         TRUE
## 4                                         TRUE
## 5                                         TRUE
## 6                                         TRUE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1                                        0.8918878
## 2                                        0.8805746
## 3                                        0.8748358
## 4                                        0.8652101
## 5                                        0.8633108
## 6                                        0.8593409
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1                                           FALSE
## 2                                           FALSE
## 3                                           FALSE
## 4                                           FALSE
## 5                                           FALSE
## 6                                           FALSE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.accurate
## 1                                             FALSE
## 2                                             FALSE
## 3                                             FALSE
## 4                                             FALSE
## 5                                             FALSE
## 6                                             FALSE
##   Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.error
## 1                                     -0.3918878
## 2                                     -0.3805746
## 3                                     -0.3748358
## 4                                     -0.3652101
## 5                                     -0.3633108
## 6                                     -0.3593409
##      USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 644     2468          R                               0.4491806
## 844     2744          R                                      NA
## 1150    4742          D                                      NA
## 1369    2429          D                               0.6985087
## 1586    5400          D                               0.5482780
## 1828    3771          D                               0.6357115
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 644                                   D
## 844                                <NA>
## 1150                               <NA>
## 1369                                  R
## 1586                                  R
## 1828                                  R
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 644                                    TRUE
## 844                                      NA
## 1150                                     NA
## 1369                                   TRUE
## 1586                                   TRUE
## 1828                                   TRUE
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 644                                   0.5508194
## 844                                          NA
## 1150                                         NA
## 1369                                  0.6985087
## 1586                                  0.5482780
## 1828                                  0.6357115
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 644                                      FALSE
## 844                                         NA
## 1150                                        NA
## 1369                                     FALSE
## 1586                                     FALSE
## 1828                                     FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.prob
## 644                                      0.4460571
## 844                                      0.4712180
## 1150                                     0.5045288
## 1369                                     0.5354487
## 1586                                     0.5725441
## 1828                                     0.6606227
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet
## 644                                         D
## 844                                         D
## 1150                                        R
## 1369                                        R
## 1586                                        R
## 1828                                        R
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err
## 644                                          TRUE
## 844                                          TRUE
## 1150                                         TRUE
## 1369                                         TRUE
## 1586                                         TRUE
## 1828                                         TRUE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err.abs
## 644                                         0.5539429
## 844                                         0.5287820
## 1150                                        0.5045288
## 1369                                        0.5354487
## 1586                                        0.5725441
## 1828                                        0.6606227
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.is.acc
## 644                                            FALSE
## 844                                            FALSE
## 1150                                           FALSE
## 1369                                           FALSE
## 1586                                           FALSE
## 1828                                           FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.accurate
## 644                                              FALSE
## 844                                              FALSE
## 1150                                             FALSE
## 1369                                             FALSE
## 1586                                             FALSE
## 1828                                             FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.error
## 644                                    -0.053942908
## 844                                    -0.028782008
## 1150                                    0.004528837
## 1369                                    0.035448749
## 1586                                    0.072544081
## 1828                                    0.160622724
##      USER_ID Party.fctr Party.fctr.RFE.X.zv.pca.rcv.glmnet.prob
## 1933    5889          D                               0.8712831
## 1934    1064          D                               0.8388104
## 1935    3374          D                               0.8388935
## 1936    1309          D                                      NA
## 1937     217          D                                      NA
## 1938    3433          D                               0.8011552
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet
## 1933                                  R
## 1934                                  R
## 1935                                  R
## 1936                               <NA>
## 1937                               <NA>
## 1938                                  R
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.err
## 1933                                   TRUE
## 1934                                   TRUE
## 1935                                   TRUE
## 1936                                     NA
## 1937                                     NA
## 1938                                   TRUE
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1933                                  0.8712831
## 1934                                  0.8388104
## 1935                                  0.8388935
## 1936                                         NA
## 1937                                         NA
## 1938                                  0.8011552
##      Party.fctr.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1933                                     FALSE
## 1934                                     FALSE
## 1935                                     FALSE
## 1936                                        NA
## 1937                                        NA
## 1938                                     FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.prob
## 1933                                     0.7958976
## 1934                                     0.8017442
## 1935                                     0.8060458
## 1936                                     0.8156517
## 1937                                     0.8327223
## 1938                                     0.8519443
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet
## 1933                                        R
## 1934                                        R
## 1935                                        R
## 1936                                        R
## 1937                                        R
## 1938                                        R
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err
## 1933                                         TRUE
## 1934                                         TRUE
## 1935                                         TRUE
## 1936                                         TRUE
## 1937                                         TRUE
## 1938                                         TRUE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err.abs
## 1933                                        0.7958976
## 1934                                        0.8017442
## 1935                                        0.8060458
## 1936                                        0.8156517
## 1937                                        0.8327223
## 1938                                        0.8519443
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.is.acc
## 1933                                           FALSE
## 1934                                           FALSE
## 1935                                           FALSE
## 1936                                           FALSE
## 1937                                           FALSE
## 1938                                           FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.accurate
## 1933                                             FALSE
## 1934                                             FALSE
## 1935                                             FALSE
## 1936                                             FALSE
## 1937                                             FALSE
## 1938                                             FALSE
##      Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.error
## 1933                                      0.2958976
## 1934                                      0.3017442
## 1935                                      0.3060458
## 1936                                      0.3156517
## 1937                                      0.3327223
## 1938                                      0.3519443

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.prob"   
## [2] "Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet"        
## [3] "Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err"    
## [4] "Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.err.abs"
## [5] "Party.fctr.Final.RFE.X.zv.pca.rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##               label step_major step_minor label_minor     bgn     end
## 5 fit.data.training          2          1           1 517.208 529.463
## 6  predict.data.new          3          0           0 529.464      NA
##   elapsed
## 5  12.255
## 6      NA

Step 3.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 143
## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## NULL
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] 0.5
## [1] "glbMdlSelId: RFE.X#zv.pca#rcv#glmnet"
## [1] "glbMdlFinId: Final.RFE.X#zv.pca#rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                      max.Accuracy.OOB max.AUCROCR.OOB
## RFE.X#zv.pca#rcv#glmnet                     0.5829596       0.5816510
## All.X#nzv.spatialSign#rcv#glmnet            0.5820628       0.5787416
## All.X#spatialSign#rcv#glmnet                0.5811659       0.5793099
## RFE.X#nzv.spatialSign#rcv#glmnet            0.5811659       0.5785672
## All.X.Inc#nzv.spatialSign#rcv#glmnet        0.5811659       0.5734523
## RFE.X#spatialSign#rcv#glmnet                0.5802691       0.5794035
## All.X#nzv#rcv#glmnet                        0.5775785       0.5766539
## RFE.X#nzv#rcv#glmnet                        0.5775785       0.5762019
## All.X#expoTrans#rcv#glmnet                  0.5775785       0.5745421
## RFE.X#expoTrans#rcv#glmnet                  0.5775785       0.5745421
## RFE.X#YeoJohnson#rcv#glmnet                 0.5775785       0.5744824
## All.X#YeoJohnson#rcv#glmnet                 0.5775785       0.5744824
## RFE.X##rcv#glmnet                           0.5766816       0.5757966
## RFE.X#zv#rcv#glmnet                         0.5766816       0.5757966
## RFE.X#BoxCox#rcv#glmnet                     0.5766816       0.5757966
## RFE.X#center#rcv#glmnet                     0.5766816       0.5757966
## RFE.X#scale#rcv#glmnet                      0.5766816       0.5757966
## RFE.X#center.scale#rcv#glmnet               0.5766816       0.5757966
## RFE.X#range#rcv#glmnet                      0.5766816       0.5757966
## RFE.X#conditionalX#rcv#glmnet               0.5766816       0.5757966
## Low.cor.X##rcv#glmnet                       0.5766816       0.5757966
## All.X##rcv#glmnet                           0.5766816       0.5757966
## All.X#zv#rcv#glmnet                         0.5766816       0.5757966
## All.X#BoxCox#rcv#glmnet                     0.5766816       0.5757966
## All.X#center#rcv#glmnet                     0.5766816       0.5757966
## All.X#scale#rcv#glmnet                      0.5766816       0.5757966
## All.X#center.scale#rcv#glmnet               0.5766816       0.5757966
## All.X#range#rcv#glmnet                      0.5766816       0.5757966
## All.X#conditionalX#rcv#glmnet               0.5766816       0.5757966
## All.X#zv.pca#rcv#glmnet                     0.5748879       0.5798104
## RFE.X#nzv.pca.spatialSign#rcv#glmnet        0.5713004       0.5742305
## All.X##rcv#glm                              0.5650224       0.5687249
## RFE.X#ica#rcv#glmnet                        0.5363229       0.5370894
## All.X#ica#rcv#glmnet                        0.5354260       0.5360626
## Interact.High.cor.Y##rcv#glmnet             0.5345291       0.5242069
## Random###myrandom_classfr                   0.5300448       0.5181895
## Max.cor.Y.rcv.1X1###glmnet                  0.5300448       0.5102459
## Max.cor.Y##rcv#rpart                        0.5300448       0.5000646
## MFO###myMFO_classfr                         0.5300448       0.5000000
## Final.RFE.X#zv.pca#rcv#glmnet                      NA              NA
##                                      max.AUCpROC.OOB max.Accuracy.fit
## RFE.X#zv.pca#rcv#glmnet                    0.5792308        0.6459313
## All.X#nzv.spatialSign#rcv#glmnet           0.5572148        0.6478785
## All.X#spatialSign#rcv#glmnet               0.5570372        0.6492270
## RFE.X#nzv.spatialSign#rcv#glmnet           0.5597528        0.6462317
## All.X.Inc#nzv.spatialSign#rcv#glmnet       0.5556503        0.6440612
## RFE.X#spatialSign#rcv#glmnet               0.5570372        0.6490772
## All.X#nzv#rcv#glmnet                       0.5550900        0.6475806
## RFE.X#nzv#rcv#glmnet                       0.5531816        0.6466826
## All.X#expoTrans#rcv#glmnet                 0.5559554        0.6463076
## RFE.X#expoTrans#rcv#glmnet                 0.5559554        0.6462327
## RFE.X#YeoJohnson#rcv#glmnet                0.5559554        0.6466820
## All.X#YeoJohnson#rcv#glmnet                0.5559554        0.6465321
## RFE.X##rcv#glmnet                          0.5588180        0.6475052
## RFE.X#zv#rcv#glmnet                        0.5588180        0.6475052
## RFE.X#BoxCox#rcv#glmnet                    0.5588180        0.6475052
## RFE.X#center#rcv#glmnet                    0.5588180        0.6475052
## RFE.X#scale#rcv#glmnet                     0.5588180        0.6475052
## RFE.X#center.scale#rcv#glmnet              0.5588180        0.6475052
## RFE.X#range#rcv#glmnet                     0.5588180        0.6475052
## RFE.X#conditionalX#rcv#glmnet              0.5588180        0.6475052
## Low.cor.X##rcv#glmnet                      0.5588180        0.6471308
## All.X##rcv#glmnet                          0.5588180        0.6471308
## All.X#zv#rcv#glmnet                        0.5588180        0.6471308
## All.X#BoxCox#rcv#glmnet                    0.5588180        0.6471308
## All.X#center#rcv#glmnet                    0.5588180        0.6471308
## All.X#scale#rcv#glmnet                     0.5588180        0.6471308
## All.X#center.scale#rcv#glmnet              0.5588180        0.6471308
## All.X#range#rcv#glmnet                     0.5588180        0.6471308
## All.X#conditionalX#rcv#glmnet              0.5588180        0.6471308
## All.X#zv.pca#rcv#glmnet                    0.5717247        0.6421158
## RFE.X#nzv.pca.spatialSign#rcv#glmnet       0.5545298        0.6480297
## All.X##rcv#glm                             0.5487933        0.6254219
## RFE.X#ica#rcv#glmnet                       0.5121737        0.5603719
## All.X#ica#rcv#glmnet                       0.5133442        0.5607462
## Interact.High.cor.Y##rcv#glmnet            0.5218093        0.6250526
## Random###myrandom_classfr                  0.4836608        0.5299798
## Max.cor.Y.rcv.1X1###glmnet                 0.4999322        0.6240737
## Max.cor.Y##rcv#rpart                       0.4999322        0.6227308
## MFO###myMFO_classfr                        0.5000000        0.5299798
## Final.RFE.X#zv.pca#rcv#glmnet                     NA        0.6272737
##                                      opt.prob.threshold.fit
## RFE.X#zv.pca#rcv#glmnet                                0.50
## All.X#nzv.spatialSign#rcv#glmnet                       0.50
## All.X#spatialSign#rcv#glmnet                           0.50
## RFE.X#nzv.spatialSign#rcv#glmnet                       0.50
## All.X.Inc#nzv.spatialSign#rcv#glmnet                   0.50
## RFE.X#spatialSign#rcv#glmnet                           0.50
## All.X#nzv#rcv#glmnet                                   0.50
## RFE.X#nzv#rcv#glmnet                                   0.50
## All.X#expoTrans#rcv#glmnet                             0.50
## RFE.X#expoTrans#rcv#glmnet                             0.50
## RFE.X#YeoJohnson#rcv#glmnet                            0.50
## All.X#YeoJohnson#rcv#glmnet                            0.50
## RFE.X##rcv#glmnet                                      0.50
## RFE.X#zv#rcv#glmnet                                    0.50
## RFE.X#BoxCox#rcv#glmnet                                0.50
## RFE.X#center#rcv#glmnet                                0.50
## RFE.X#scale#rcv#glmnet                                 0.50
## RFE.X#center.scale#rcv#glmnet                          0.50
## RFE.X#range#rcv#glmnet                                 0.50
## RFE.X#conditionalX#rcv#glmnet                          0.50
## Low.cor.X##rcv#glmnet                                  0.50
## All.X##rcv#glmnet                                      0.50
## All.X#zv#rcv#glmnet                                    0.50
## All.X#BoxCox#rcv#glmnet                                0.50
## All.X#center#rcv#glmnet                                0.50
## All.X#scale#rcv#glmnet                                 0.50
## All.X#center.scale#rcv#glmnet                          0.50
## All.X#range#rcv#glmnet                                 0.50
## All.X#conditionalX#rcv#glmnet                          0.50
## All.X#zv.pca#rcv#glmnet                                0.50
## RFE.X#nzv.pca.spatialSign#rcv#glmnet                   0.50
## All.X##rcv#glm                                         0.50
## RFE.X#ica#rcv#glmnet                                   0.50
## All.X#ica#rcv#glmnet                                   0.50
## Interact.High.cor.Y##rcv#glmnet                        0.50
## Random###myrandom_classfr                              0.55
## Max.cor.Y.rcv.1X1###glmnet                             0.45
## Max.cor.Y##rcv#rpart                                   0.50
## MFO###myMFO_classfr                                    0.50
## Final.RFE.X#zv.pca#rcv#glmnet                          0.50
##                                      opt.prob.threshold.OOB
## RFE.X#zv.pca#rcv#glmnet                                0.50
## All.X#nzv.spatialSign#rcv#glmnet                       0.60
## All.X#spatialSign#rcv#glmnet                           0.60
## RFE.X#nzv.spatialSign#rcv#glmnet                       0.60
## All.X.Inc#nzv.spatialSign#rcv#glmnet                   0.60
## RFE.X#spatialSign#rcv#glmnet                           0.60
## All.X#nzv#rcv#glmnet                                   0.60
## RFE.X#nzv#rcv#glmnet                                   0.55
## All.X#expoTrans#rcv#glmnet                             0.55
## RFE.X#expoTrans#rcv#glmnet                             0.55
## RFE.X#YeoJohnson#rcv#glmnet                            0.55
## All.X#YeoJohnson#rcv#glmnet                            0.55
## RFE.X##rcv#glmnet                                      0.55
## RFE.X#zv#rcv#glmnet                                    0.55
## RFE.X#BoxCox#rcv#glmnet                                0.55
## RFE.X#center#rcv#glmnet                                0.55
## RFE.X#scale#rcv#glmnet                                 0.55
## RFE.X#center.scale#rcv#glmnet                          0.55
## RFE.X#range#rcv#glmnet                                 0.55
## RFE.X#conditionalX#rcv#glmnet                          0.55
## Low.cor.X##rcv#glmnet                                  0.55
## All.X##rcv#glmnet                                      0.55
## All.X#zv#rcv#glmnet                                    0.55
## All.X#BoxCox#rcv#glmnet                                0.55
## All.X#center#rcv#glmnet                                0.55
## All.X#scale#rcv#glmnet                                 0.55
## All.X#center.scale#rcv#glmnet                          0.55
## All.X#range#rcv#glmnet                                 0.55
## All.X#conditionalX#rcv#glmnet                          0.55
## All.X#zv.pca#rcv#glmnet                                0.50
## RFE.X#nzv.pca.spatialSign#rcv#glmnet                   0.55
## All.X##rcv#glm                                         0.65
## RFE.X#ica#rcv#glmnet                                   0.55
## All.X#ica#rcv#glmnet                                   0.55
## Interact.High.cor.Y##rcv#glmnet                        0.70
## Random###myrandom_classfr                              0.55
## Max.cor.Y.rcv.1X1###glmnet                             0.65
## Max.cor.Y##rcv#rpart                                   0.65
## MFO###myMFO_classfr                                    0.50
## Final.RFE.X#zv.pca#rcv#glmnet                            NA
## [1] "RFE.X#zv.pca#rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   D   R
##         D 379 212
##         R 253 271
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## No         883.9618       239.90734       1138.9447              NA
## NA         821.7109       209.08425       1036.9025              NA
## Yes        200.4801        85.28436        335.1653              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## No       0.4403773      0.4466368      0.4468391   1961      223      399
## NA       0.3920952      0.3928251      0.3929598   1746      357      190
## Yes      0.1675275      0.1605381      0.1602011    746      216        7
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## No     498     1038     1421    622   1961    622   2459        0.4817416
## NA     438     1171     1013    547   1746    547   2184        0.4773613
## Yes    179      742      183    223    746    223    925        0.4764489
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## No         0.4507709               NA        0.4631739
## NA         0.4706248               NA        0.4747722
## Yes        0.2687400               NA        0.3623408
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      1906.152762       534.275955      2511.012416               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      4453.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       796.000000       596.000000      1115.000000      2951.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      2617.000000      1392.000000      4453.000000      1392.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      5568.000000         1.435552         1.190136               NA 
## err.abs.trn.mean 
##         1.300287
## [1] "Features Importance for selected models:"
##       RFE.X.zv.pca.rcv.glmnet.imp Final.RFE.X.zv.pca.rcv.glmnet.imp
## PC8                  1.000000e+02                         0.0000000
## PC5                  7.995721e+01                        49.8376492
## PC10                 6.660214e+01                        50.5685756
## PC15                 6.321726e+01                        41.7791632
## PC9                  5.584978e+01                        11.3902546
## PC26                 5.050796e+01                         0.0000000
## PC3                  4.474537e+01                        41.9809181
## PC14                 4.398423e+01                        17.6735434
## PC57                 3.105066e+01                         0.0000000
## PC68                 2.684775e+01                         0.0000000
## PC30                 2.569378e+01                         0.0000000
## PC88                 2.477208e+01                        20.2241471
## PC128                2.424400e+01                         6.1221525
## PC25                 2.371905e+01                         0.0000000
## PC19                 2.341047e+01                         0.5460154
## PC124                2.213194e+01                         0.0000000
## PC52                 2.128227e+01                         0.0000000
## PC16                 2.054784e+01                        14.1567136
## PC142                1.751430e+01                         1.4825128
## PC114                1.650518e+01                         0.0000000
## PC40                 1.496730e+01                         0.0000000
## PC23                 1.488607e+01                        19.7235979
## PC22                 1.452383e+01                         0.0000000
## PC100                1.448174e+01                         3.4824249
## PC138                1.321459e+01                         0.0000000
## PC126                1.295801e+01                         0.0000000
## PC45                 1.271547e+01                         0.0000000
## PC101                1.257034e+01                         0.0000000
## PC2                  1.110115e+01                        10.3218729
## PC11                 1.060646e+01                         0.0000000
## PC77                 8.954286e+00                        22.6058956
## PC6                  8.539962e+00                        14.8224130
## PC27                 6.583676e+00                        12.0058280
## PC92                 6.234515e+00                        43.7560091
## PC4                  4.495763e+00                        20.2437893
## PC113                2.752156e+00                        21.7031007
## PC12                 1.279928e-01                        43.7290631
## PC120                7.951659e-03                        12.5092251
## PC7                  0.000000e+00                       100.0000000
## PC97                 0.000000e+00                        29.8435851
## PC31                 0.000000e+00                        24.4858038
## PC56                 0.000000e+00                        20.2158202
## PC38                 0.000000e+00                        19.8200242
## PC42                 0.000000e+00                        17.3445901
## PC86                 0.000000e+00                        17.2175203
## PC66                 0.000000e+00                        11.0304029
## PC28                 0.000000e+00                        10.3260467
## [1] "glbObsNew prediction stats:"
## 
##   D   R 
## 796 596
##                  label step_major step_minor label_minor     bgn     end
## 6     predict.data.new          3          0           0 529.464 573.709
## 7 display.session.info          4          0           0 573.710      NA
##   elapsed
## 6  44.245
## 7      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##               label step_major step_minor label_minor     bgn     end
## 1      fit.models_1          1          0           0   7.720 319.664
## 4 fit.data.training          2          0           0 339.957 517.207
## 6  predict.data.new          3          0           0 529.464 573.709
## 2        fit.models          1          1           1 319.665 334.110
## 5 fit.data.training          2          1           1 517.208 529.463
## 3        fit.models          1          2           2 334.111 339.957
##   elapsed duration
## 1 311.944  311.944
## 4 177.251  177.250
## 6  44.245   44.245
## 2  14.445   14.445
## 5  12.255   12.255
## 3   5.846    5.846
## [1] "Total Elapsed Time: 573.709 secs"

##                    label step_major step_minor label_minor     bgn     end
## 9   fit.models_1_preProc          1          8     preProc 318.316 319.658
## 5 fit.models_1_All.X.Inc          1          4       setup 317.999 318.291
## 1       fit.models_1_bgn          1          0       setup 317.965 317.978
## 2     fit.models_1_All.X          1          1       setup 317.978 317.988
## 6 fit.models_1_All.X.Inc          1          5      glmnet 318.292 318.301
## 8     fit.models_1_RFE.X          1          7      glmnet 318.308 318.316
## 7     fit.models_1_RFE.X          1          6       setup 318.302 318.308
## 3     fit.models_1_All.X          1          2      glmnet 317.988 317.993
## 4     fit.models_1_All.X          1          3         glm 317.994 317.998
##   elapsed duration
## 9   1.342    1.342
## 5   0.292    0.292
## 1   0.013    0.013
## 2   0.010    0.010
## 6   0.009    0.009
## 8   0.008    0.008
## 7   0.006    0.006
## 3   0.005    0.005
## 4   0.004    0.004
## [1] "Total Elapsed Time: 319.658 secs"